I’ve been working on an open-source reference implementation of an agentic DevSecOps container-release pipeline.

The goal was to keep security decisions deterministic while using an AI agent only to explain findings, prioritize remediation, and make scanner output easier for engineering teams to act on.

The pipeline includes:

• zSonarQube source-code and quality analysis
• Trivy Dockerfile and Kubernetes misconfiguration scanning
• Local container-image build
• Trivy vulnerability and embedded-secret scanning
• Deterministic release-policy evaluation
• Claude Sonnet 5 or Google ADK advisory triage
• HTML, PDF, Markdown, JSON, and SARIF reports
• Email delivery through Resend
• Discord notifications
• Protected human approval
• Docker Hub publishing only after authorization

For the demonstration, the workflow processed an intentionally insecure training fixture and found:

• 13 SonarQube findings
• 25 configuration misconfigurations
• 347 container-image vulnerabilities
• 372 total Trivy findings
• 74 policy-blocking findings

The workflow continued scanning long enough to collect complete evidence, but the deterministic policy kept the release BLOCKED. Human approval and image publishing were skipped.

The Claude advisory successfully analyzed a bounded subset of the scanner evidence. It prioritized remediation and explained possible attack paths, but it could not approve, reject, waive, or override the deterministic policy.

The design principle is:

The pipeline generates three primary reports:

1. Code and configuration security report
2. Container vulnerability and secret report
3. Consolidated release-security report

Repository:
https://github.com/DevOpsAIguru123/awesome-agentic-devops/tree/main/agents/container-image-release-advisor

Demonstration run:
https://github.com/DevOpsAIguru123/awesome-agentic-devops/actions/runs/30590931717

This is an open-source reference implementation rather than a universal production template. Production adoption would require organization-specific policies, isolated builds, artifact signing, provenance, registry controls, and continuous monitoring.

I’d appreciate feedback on:

• Whether AI advisory analysis belongs inside the release pipeline or should run asynchronously
• How you deliver security findings without overwhelming developers
• Which additional supply-chain controls you would add
• Whether a deliberately blocked security workflow should appear red or use a successful “blocked as designed” conclusion